This document discusses MEILI, a smartphone-based system for semi-automatically collecting activity-travel diaries. It conducted a trial of MEILI in Stockholm, collecting data from 30 users over 5 days and comparing it to traditional paper surveys. The trial found that MEILI collected more detailed trip and waiting time data but users reported issues with battery life and finding points of interest. Based on lessons learned, improvements were made to the user interface, data model, and artificial intelligence methods to better segment trips and infer travel modes. Overall, MEILI provides a viable modern alternative for more detailed and large-scale travel diary collection compared to traditional methods.

1. Lessons from a trial of MEILI
a smartphone based semi-automatic activity-travel diary
collector, in Stockholm city, Sweden
Y. Susilo2
, A. C. Prelipcean1,2
, G. Gid´ofalvi1
,
A. Allstr¨om3
, I. Kristoffersson 3
, J. Widell3
1Division of Geoinformatics, KTH Royal Institute of Technology
2Department of Transportation Science, KTH Royal Institute of Technology
3Sweco Transport System AB
acpr@kth.se
@Adi Prelipcean
adrianprelipcean.github.io
12 July 2016

2. Outline
This presentation will be about:
1. Travel diaries
– Definition and uses
– Collection methods
2. MEILI: an (activity) travel diary collection, annotation
and automation system
– System Overview
– Collected Data
3. Case study and Lessons Learned
– Experimental setup
– Results
– Lessons learned: survey schedule
– Lessons learned: user experience
– Lessons learned: data collection
4. Summary and conclusions
2

3. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
3

4. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
to predict the effect of implementing new transportation
policies or changing the transportation infrastructure, or
3

5. Travel behaviour
How do we use travel behaviour?
Some of the main reasons for analyzing travel behaviour are:
to investigate the reasons and mechanisms that underlie
an individual’s travel decision making process,
to predict the effect of implementing new transportation
policies or changing the transportation infrastructure, or
to understand the dynamic of transportation movement
within study areas.
3

6. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
The destination of a trip
Img: http://soarministries.com/hp_wordpress/wp-content/uploads/2011/08/Destinations-Icon.jpg 4

7. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
The destination of a trip
The trip’s purpose
Img: https://cdn2.vox-cdn.com/thumbor/93Yaxs7y3Tb8tzFfppyRsSn_yN8=/1020x0/cdn0.vox-cdn.com/ 4

8. (Activity) Travel diaries
What are they?
A way of summarizing where, why and how a user traveled
during a defined time frame by specifying:
The destination of a trip
The trip’s purpose
The means of transportation, i.e., trip legs
Img: https://d3ui957tjb5bqd.cloudfront.net/images/screenshots/products/4/42/42990/ 4

9. (Activity) Travel diaries
How to collect them?
Traditionally - Users declare what they have done in a
survey, e.g., PP or CATI
Img: http://www.schoolsurveyexperts.co.uk/i/photos/paper_survey.jpg
5

10. (Activity) Travel diaries
How to collect them?
Traditionally - Users declare what they have done in a
survey, e.g., PP or CATI
New methods - E.g., GPS collection + Web and Mobile
GIS based interaction
5

11. MEILI: an (activity) travel diary collection,
annotation and automation system
What is MEILI?
MEILI-travel diary collection, annotation & automation system
MEILI Mobility Collector, which is a smartphone app
that collects trajectories fused with accelerometer
readings from users.
MEILI Travel Diary, which is a web app that allows
users to annotate trajectories into travel diaries.
MEILI Database, which is the database that stores both
the collected and annotated data.
MEILI API, which securely connects the Mobility
Collector and the Travel Diary to the Database.
MEILI AI, which is an Artificial Intelligence module that
automatically annotates the trajectories collected by
users. 6

12. MEILI Workflow
7

13. Case study
Collection details
Stockholm, Sweden
Traditional PP collection: 42 users, 29th September 2014
Modern MEILI collection: 30 users, 29 Sept - 05 Oct 2014
28 users collected with both PP and MEILI
users work in transportation
PP collected 94 trips
MEILI collected 87 trips (on the 29th) and 608 trips
(during the whole period)
trip correspondence between PP and MEILI was found
based on temporal co-occurance and purpose matching
8

14. Case study
A brief overview of the collected data
Statistics computed on intersection set are similar. MEILI offers more
information, i.e., waiting time and spatial and temporal indicator values.
9

15. Case study
A brief overview of the collected data
MEILI captures tripleg-level information, and identifies potential
problems with the collection.
9

16. Case study
A brief overview of the collected data
The difference between the time a user has to wait for ”personal”
transportation modes and public transportation modes.
When waiting for public transportation modes, the time spent waiting
amounts for roughly 10-20% of the whole trip duration.
9

17. User feedback
34 users provided feedback on:
User Interface (based on map operations)
10

18. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
10

19. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
MEILI Mobility Collector’s effect on battery life (50% reported
little effect on battery life, 25% reported a major effect on battery
life)
10

20. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
MEILI Mobility Collector’s effect on battery life (50% reported
little effect on battery life, 25% reported a major effect on battery
life)
MEILI Travel Diary trip and tripleg annotation process
10

21. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
MEILI Mobility Collector’s effect on battery life (50% reported
little effect on battery life, 25% reported a major effect on battery
life)
MEILI Travel Diary trip and tripleg annotation process
selecting a trip’s destination from the POI dataset (47% found it
difficult to find a POI and 20% would like more POI alternatives)
10

22. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
MEILI Mobility Collector’s effect on battery life (50% reported
little effect on battery life, 25% reported a major effect on battery
life)
MEILI Travel Diary trip and tripleg annotation process
selecting a trip’s destination from the POI dataset (47% found it
difficult to find a POI and 20% would like more POI alternatives)
wrongfully detected trips by MEILI (60% reported at least one
wrongfully detected trip)
10

23. User feedback
34 users provided feedback on:
MEILI Mobility Collector ease of install (85% reported no
problems)
MEILI Mobility Collector’s effect on battery life (50% reported
little effect on battery life, 25% reported a major effect on battery
life)
MEILI Travel Diary trip and tripleg annotation process
selecting a trip’s destination from the POI dataset (47% found it
difficult to find a POI and 20% would like more POI alternatives)
wrongfully detected trips by MEILI (60% reported at least one
wrongfully detected trip)
collection integrity (67% found smartphone collection more
invasive)
10

24. Lessons learned
User Interface for Annotations
improved the UI / UX based on user feedback and
discussions with UI / UX experts
11

25. Lessons learned
User Interface for Annotations
improved the UI / UX based on user feedback and
discussions with UI / UX experts
11

26. Lessons learned
User Interface for Annotations
improved the UI / UX based on user feedback and
discussions with UI / UX experts
implemented sequential navigation through trips
11

27. Lessons learned
User Interface for Annotations
improved the UI / UX based on user feedback and
discussions with UI / UX experts
implemented sequential navigation through trips
implemented the crowdsourcing of public and
transportation POIs (e.g., if one users declares a missing
bus stop it is available for the rest)
11

28. Lessons learned
User Interface for Annotations
improved the UI / UX based on user feedback and
discussions with UI / UX experts
implemented sequential navigation through trips
implemented the crowdsourcing of public and
transportation POIs (e.g., if one users declares a missing
bus stop it is available for the rest)
complemented the web interface operations:
– Create - insertion of trips, triplegs, locations, POIs
– Read - pagination operations between consecutive trips
– Update - update of trips, triplegs, locations, POIs
– Delete - deletion of trips, triplegs, locations, POIs
11

29. Storage
changed the data model from a point-based model into a
period-based model
– trips and triplegs are explicitly modeled in the period-based
model
– added the concept of passive period between consecutive trips
that describes the time spent performing an activity
– added the concept of passive period between consecutive
triplegs that describes the time spent waiting for
transportation mode
the new data model has been indexed and optimized (see
Prelipcean et al. 2016 - MEILI: a travel diary collection,
annotation and automation system, presented at Mobile
Tartu 2016, submitted to Journal of Transport
Geography)
12

30. Inference Methods and Artificial Intelligence
Some words on AI and segmentation tasks
Trip segmentation - based on heuristics rules obtains
reasonable accuracy (P=96.7%, R=73.8%)
Tripleg segmentation - changed from implicit to explicit
by detecting sequences of GPS points with low deviation
in movement characteristics - f(speed, accelerometer)
Travel mode inference - changed from point-based
Random Forest to a Nearest-Neighbor point-based
consensus within a period
Destination inference - difficult to obtain high precision
with limited user history since most personal POIs (work
and home locations) are missing from the database
Purpose inference - limited by the performance of the
destination inference
13

31. What do we gain from using modern methods?
More detailed trip and trip-leg level information that is
missing in traditional methods
The data are already centralized and stored using a data
model that reduces the complexity of performing travel
diary specific operations
Can use spatial and temporal indicators to assess the
quality of the collected data and propose ground truth
candidates
MEILI can be reused for data collection in different
countries if localized
MEILI is a viable alternative to study travel diaries on a
broader scale (not limited to regions / countries) and on
a wider time frame (not limited to one day / week)
14

32. Summary
introduced MEILI, an open source travel diary collection,
annotation and automation system.
presented MEILI’s modular architecture that isolates the
development process to each module
provided brief discussions on inferences and AI used in
travel diaries
provided a set of valuable lessons learned during case
studies of applying MEILI to collect travel diaries
15

33. Acknowledgements and References
Acknowledgments
This work was partly supported by Trafikverket (Swedish
Transport Administration) under Grant “TRV 2014/10422”.
References
source code for MEILI https://github.com/Badger-MEILI
Mobility Collector - Prelipcean, A. C., Gid´ofalvi, G., & Susilo, Y. O.
(2014). Mobility collector. Journal of Location Based Services,
8(4), 229-255.
a framework for the comparison of travel diary collection systems -
Prelipcean, A. C., Gid´ofalvi, G., & Susilo, Y. O. (2015).
Comparative framework for activity-travel diary collection systems.
In Models and Technologies for Intelligent Transportation Systems
(MT-ITS), 2015 International Conference on (pp. 251-258). IEEE.
on AI performance measures relevant to travel diaries - Prelipcean,
A. C., Gid´ofalvi, G., & Susilo, Y. O. (2016). Measures of transport
mode segmentation of trajectories. International Journal of
Geographical Information Science, 30(9), 1763-1784.
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34. Thank you for your attention!
Questions and Discussions
Adrian C. Prelipcean
Phd Student
Division of Geoinformatics
KTH, Royal Institute of Technology
http://adrianprelipcean.github.io/
acpr@kth.se
@Adi Prelipcean
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